James Humann
Papers
2
Total Citations
22
H-Index
2
About
James Humann’s research lies at the critical intersection of multi-agent systems, autonomous vehicle coordination, and energy-aware logistics. His work addresses a fundamental challenge in robotics: how to keep a fleet of battery-limited Unmanned Aerial Vehicles (UAVs) operational over extended missions. Humann’s most influential contribution is his development of an agent-based modeling framework for the multi-UAV rendezvous recharging problem (2023, 18 citations), which provides a scalable, decentralized solution for coordinating aerial drones with ground-based mobile recharging stations. Building on this, his risk-aware resource allocation study (2022) introduces robust decision-making under uncertainty for cooperative UAV-UGV systems, where ground vehicles serve as both transporters and power sources. Though early in his career, Humann’s work has already garnered attention for its practical relevance to persistent surveillance, disaster response, and precision agriculture. His frameworks offer a blueprint for designing resilient, energy-autonomous drone networks—a key enabler for next-generation autonomous field operations. For students and researchers, Humann’s research exemplifies how formal modeling can solve real-world constraints in multi-robot systems.
Research Focus
Key Achievements
Top Papers
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- 2